如何在 ML Azure Pipeline 中使用环境

Question

背景

我已经从 condaenvironment.yml加上一些 docker 配置和环境变量创建了一个 ML Workspace 环境。我可以从 Python 笔记本中访问它：

env = Environment.get(workspace=ws, name='my-environment', version='1')

我可以成功地使用它来运行 Python 脚本作为实验，即

runconfig = ScriptRunConfig(source_directory='script/', script='my-script.py', arguments=script_params)
runconfig.run_config.target = compute_target
runconfig.run_config.environment = env
run = exp.submit(runconfig)

问题

我现在想像流水线一样运行这个相同的脚本，这样我就可以用不同的参数触发多次运行。我创建的管道如下：

pipeline_step = PythonScriptStep(
    source_directory='script', script_name='my-script.py',
    arguments=['-a', param1, '-b', param2],
    compute_target=compute_target,
    runconfig=runconfig
)
steps = [pipeline_step]
pipeline = Pipeline(workspace=ws, steps=steps)
pipeline.validate()

当我然后尝试运行管道时：

pipeline_run = Experiment(ws, 'my_pipeline_run').submit(
    pipeline, pipeline_parameters={...}
)

我收到以下错误： Response status code does not indicate success: 400 (Conda dependencies were not specified. Please make sure that all conda dependencies were specified i).

当我查看在 Azure 门户中运行的管道时，似乎还没有选择环境：我的 conda 依赖项都没有配置，因此代码没有运行。我究竟做错了什么？

Answer 1

你快到了，但你需要使用RunConfiguration而不是ScriptRunConfig. 更多信息在这里

from azureml.core.runconfig import RunConfiguration

env = Environment.get(workspace=ws, name='my-environment', version='1')
# create a new runconfig object
runconfig = RunConfiguration()
runconfig.environment = env

pipeline_step = PythonScriptStep(
    source_directory='script', script_name='my-script.py',
    arguments=['-a', param1, '-b', param2],
    compute_target=compute_target,
    runconfig=runconfig
)

pipeline = Pipeline(workspace=ws, steps=[pipeline_step])

pipeline_run = Experiment(ws, 'my_pipeline_run').submit(pipeline)